





'''simple docstring'''


import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
    import torch

    from transformers import XLMRobertaModel






@require_sentencepiece
@require_tokenizers
@require_torch
class     lowerCAmelCase__ (				unittest.TestCase    ):

       '''simple docstring'''

       @slow
       def 			__snake_case      ( self					:				List[Any]						):
              UpperCAmelCase  =						XLMRobertaModel.from_pretrained('''xlm-roberta-base'''						)
              UpperCAmelCase  =						torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]						)
              # The dog is cute and lives in the garden house

              UpperCAmelCase  =						torch.Size((1, 12, 768)						)  # batch_size, sequence_length, embedding_vector_dim
              UpperCAmelCase  =						torch.tensor(
                  [[-0.0_101, 0.1_218, -0.0_803, 0.0_801, 0.1_327, 0.0_776, -0.1_215, 0.2_383, 0.3_338, 0.3_106, 0.0_300, 0.0_252]]						)
              #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
              #  xlmr.eval()
              #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
              with torch.no_grad():
                     UpperCAmelCase  =						model(a__						)['''last_hidden_state'''].detach()
              self.assertEqual(output.shape       ,     a__						)
              # compare the actual values for a slice of last dim
              self.assertTrue(torch.allclose(output[:, :, -1]       ,     a__       ,     atol=1e-3						)						)



       @slow
       def 			__snake_case      ( self					:				Union[str, Any]						):
              UpperCAmelCase  =						XLMRobertaModel.from_pretrained('''xlm-roberta-large'''						)
              UpperCAmelCase  =						torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]						)
              # The dog is cute and lives in the garden house

              UpperCAmelCase  =						torch.Size((1, 12, 1024)						)  # batch_size, sequence_length, embedding_vector_dim
              UpperCAmelCase  =						torch.tensor(
                  [[-0.0_699, -0.0_318, 0.0_705, -0.1_241, 0.0_999, -0.0_520, 0.1_004, -0.1_838, -0.4_704, 0.1_437, 0.0_821, 0.0_126]]						)
              #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
              #  xlmr.eval()
              #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
              with torch.no_grad():
                     UpperCAmelCase  =						model(a__						)['''last_hidden_state'''].detach()
              self.assertEqual(output.shape       ,     a__						)
              # compare the actual values for a slice of last dim
              self.assertTrue(torch.allclose(output[:, :, -1]       ,     a__       ,     atol=1e-3						)						)








'''simple docstring'''


import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate





@dataclass(frozen=__magic_name__			)
class  __lowercase    (	__magic_name__			):
   _a    						=   field(default="""audio-classification"""							,				metadata={"""include_in_asdict_even_if_is_default""": True}			)
   _a    						=   Features({"""audio""": Audio()}			)
   _a    						=   Features({"""labels""": ClassLabel}			)
   _a    						=   "audio"
   _a    						=   "labels"







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Dict:
     if self.label_column not in features:
       raise ValueError(f"Column {self.label_column} is not present in features."   )
     if not isinstance(features[self.label_column]   ,				UpperCamelCase   ):
       raise ValueError(f"Column {self.label_column} is not a ClassLabel."   )
     __a								=       copy.deepcopy(self   )
     __a								=       self.label_schema.copy()
     __a								=       features[self.label_column]
     __a								=       label_schema
     return task_template







   @property
   def 		UpperCamelCase__      (						self   )					->     Dict[str, str]:
     return {
         self.audio_column: "audio",
         self.label_column: "labels",
     }



